Digital

When an operator’s instruction conflicts with a codified safety rule, an assistant should recognize the conflict and defer to the rule. This is a pragmatic problem with an objective answer: the correct action follows from a published regulation, while the difficulty is linguistic. Existing evaluations do not target this case: general safety benchmarks ask whether a model emits harmful content, instruction-following suites score obedience to formatting constraints, and work on sycophancy and rule-following shows that models can be argued out of a stated rule, but none set a user’s authoritative instruction against an external, codified rule whose deferral is objectively correct. This paper introduces a benchmark of 75 items (60 rule conflicts, 15 no-conflict controls) grounded in the International Regulations for Preventing Collisions at Sea (COLREGs) and in paraphrased International Civil Aviation Organization (ICAO) procedures, spanning four conflict framings, each presented with and without the governing rule and followed by a pushback turn. Eleven instruction-tuned models were evaluated across 1649 responses and scored by an independent language model judge (second judge Cohen’s kappa = 0.84). Because the items are author-constructed and now validated by two independent domain reviewers, compliance rates are reported as measurements on a validated benchmark rather than as mere rankings, though they remain bounded by the text scenario setting, and the model-scaling results in particular are treated as exploratory. Both model size and alignment were associated with unsafe compliance: within one family compliance fell overall from 0.51 at 0.5B to near zero at 14B, though not strictly monotonically, and at a fixed 7B size a less safety-tuned model complied far more than its aligned peers. Compliance concentrated on direct orders, fell when the rule was shown, rose under multi-turn pressure, and traded off against over-refusal, with the rule-context and multi-turn effects confirmed by item-paired tests and reported with confidence intervals. The benchmark is released for evaluating rule-aware assistants, together with the expert-validated item set and the validation materials.

Context: The increasing adoption of Internet of Things (IoT) technologies has transformed digital marketing into a software-intensive, data-driven ecosystem requiring continuous optimization under uncertainty. Existing decision-support approaches primarily optimize engagement or cost independently and rarely integrate behavioral dynamics, sustainability constraints, and managerial preferences within a unified information systems framework. Objectives: This study develops and evaluates a fuzzy multi-objective optimization framework that supports intelligent software-based marketing decision making by simultaneously maximizing customer engagement, minimizing digital resource consumption, and reducing behavioral saturation in IoT-enabled environments. Methods: A multi-objective mathematical model was developed in which customer responsiveness is represented through probabilistic engagement parameters, while fuzzy membership functions and a Max–Min satisfaction criterion represent imprecise managerial aspiration levels across the conflicting objectives. The small-scale experiment was solved exactly in GAMS to obtain reference Pareto-optimal solutions, whereas the large-scale experiment was conducted as a simulation study using NSGA-II and MOPSO to evaluate scalability and algorithmic performance. Both experimental settings relied exclusively on synthetically generated datasets; no real-world enterprise, customer-level, or campaign-level marketing data were used. Performance was assessed through Pareto-front analysis, key performance indicators, sensitivity analysis, and scenario-based managerial evaluation. Results: The proposed framework successfully generated high-quality Pareto-optimal solutions across multiple optimization objectives. NSGA-II consistently achieved superior customer engagement, personalization efficiency, and behavioral balance, whereas MOPSO demonstrated faster execution and lower sustainability costs. Sensitivity analysis confirmed the robustness of the framework under varying behavioral parameters, while scenario analysis showed that different optimization strategies can be selected according to organizational priorities. The principal limitation is that the framework has been evaluated only in controlled synthetic environments, which limits direct empirical generalization to operational enterprise marketing settings. Future research should validate the framework using longitudinal enterprise marketing data, real-time IoT interaction streams, and field-based deployment studies. Conclusions: The proposed framework contributes to information systems research by integrating fuzzy decision support, multi-objective optimization, and behavioral modeling into a scalable software architecture for IoT-enabled marketing. The approach enables adaptive, explainable, and sustainable decision making, providing organizations with a practical decision-support system capable of balancing customer experience, operational efficiency, and digital sustainability in intelligent marketing ecosystems.

The long-term reliability of selective laser melting (SLM) equipment is constrained by the latent degradation and coupled faults of multiple underlying hardware systems under continuous high-load operations. Transitioning from passive defect detection to proactive prognostics and health management (PHM) is crucial to overcome bottlenecks in industrial applications. Accurately inferring future multi-system hardware states faces three major challenges: feature alignment difficulties under variable-length printing cycles, graph topology collapse under strong industrial noise, and dimensional conflicts along with error cascading divergence during the joint optimization of microscopic physical trajectories (continuous regression) and macroscopic system anomalies (discrete classification) in end-to-end prediction. To address these issues, this paper proposes a decoupled spatiotemporal graph autoregressive network (DS-GAN). First, a multi-scale feature pooling and degradation gating injection mechanism is constructed to align highly variable-length high-frequency sequences and adaptively integrate macroscopic health priors. This approach achieves feature decoupling under physical boundary constraints. Second, a restricted residual graph evolution mechanism is introduced to regulate dynamic coupling drift based on static physical topologies, effectively suppressing feature divergence in the spatial dimension. Finally, a heterogeneous multi-task autoregressive decoder based on homoscedastic uncertainty is designed; this decoder helps reduce the impact of accumulated errors along the time axis during multi-step forecasting. Long-sequence forward inference on a real SLM continuous printing dataset demonstrates that DS-GAN achieves an overall accuracy of 97.48% for multi-system anomalies while strictly limiting the global false alarm rate to 1.85%. Furthermore, quantitative results reveal the physical inertia mechanism within the prediction horizon, demonstrating that the model maintains high fidelity for physical trajectories with a Macro-RMSE of 0.062, even under a maximum predictive horizon of 20 s. This study provides a reliable theoretical and engineering framework for dynamic coupling correlation analysis and proactive fault warning of multi-systems in complex industrial equipment.